Subtopic Deep Dive
Technology Adoption of Service Robots
Research Guide
What is Technology Adoption of Service Robots?
Technology Adoption of Service Robots examines factors influencing consumer and organizational acceptance of robots in service sectors like hospitality, healthcare, and tourism using models such as TAM and UTAUT.
Researchers apply TAM and UTAUT to identify drivers like perceived usefulness and barriers like trust deficits in robot deployment (Blut et al., 2021; Pillai & Sivathanu, 2020). Studies span hotels, restaurants, and healthcare, with meta-analyses on anthropomorphism's role (Blut et al., 2021, 922 citations). Longitudinal data and surveys assess adoption rates amid labor shortages (Ivanov et al., 2019).
Why It Matters
This subtopic guides robot deployment strategies in labor-short sectors, reducing consumer resistance through anthropomorphic designs (Blut et al., 2021). Firms in hospitality leverage trust-building features to boost adoption, cutting costs (van Pinxteren et al., 2019). Policymakers use these insights for AI integration in tourism, enhancing efficiency (Buhalis et al., 2019; Ivanov et al., 2019). Meta-analyses inform scalable interventions across services (Blut et al., 2021).
Key Research Challenges
Consumer Trust Deficits
Users hesitate to adopt humanoid robots due to low trust, despite human-like features (van Pinxteren et al., 2019, 473 citations). Surveys show trust mediates intentions but requires empathy cues (de Kervenoael et al., 2019). Interventions like information sharing show mixed longitudinal results.
Anthropomorphism Variability
Meta-analysis reveals inconsistent effects of robot anthropomorphism on acceptance across chatbots and physical robots (Blut et al., 2021, 922 citations). Physical embodiments outperform virtual in services but vary by context (Tung & Au, 2018). Standardization remains elusive.
Context-Specific Barriers
Adoption models like TAM need extensions for tourism chatbots and robots (Pillai & Sivathanu, 2020, 795 citations). Organizational hurdles in hospitality include high costs and skill gaps (Ivanov et al., 2019, 419 citations). Cultural factors complicate global deployment.
Essential Papers
Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes et al. · 2023 · International Journal of Information Management · 3.1K citations
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contex...
Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI
Markus Blut, Cheng Wang, Nancy V. Wünderlich et al. · 2021 · Journal of the Academy of Marketing Science · 922 citations
Technological disruptions in services: lessons from tourism and hospitality
Dimitrios Buhalis, Tracy Harwood, Vanja Bogicevic et al. · 2019 · Journal of service management · 826 citations
Purpose Technological disruptions such as the Internet of Things and autonomous devices, enhanced analytical capabilities (artificial intelligence) and rich media (virtual and augmented reality) ar...
Adoption of AI-based chatbots for hospitality and tourism
Rajasshrie Pillai, Brijesh Sivathanu · 2020 · International Journal of Contemporary Hospitality Management · 795 citations
Purpose This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending ...
Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots
Ronan de Kervenoael, Rajibul Hasan, Alexandre Schwob et al. · 2019 · Tourism Management · 536 citations
AI in marketing, consumer research and psychology: A systematic literature review and research agenda
Marcello M. Mariani, Rodrigo Perez‐Vega, Jochen Wirtz · 2021 · Psychology and Marketing · 484 citations
Abstract This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By ...
Trust in humanoid robots: implications for services marketing
Michelle M. E. van Pinxteren, Ruud W.H. Wetzels, Jessica Rüger et al. · 2019 · Journal of Services Marketing · 473 citations
Purpose Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adop...
Reading Guide
Foundational Papers
No pre-2015 foundational papers available; start with highest-cited recent: Blut et al. (2021) for anthropomorphism meta-analysis baseline.
Recent Advances
Prioritize Dwivedi et al. (2023, 3140 citations) for AI policy implications; Carvalho & Ivanov (2023, 415 citations) for ChatGPT in tourism adoption.
Core Methods
Core techniques: TAM/UTAUT extensions via SEM (Pillai & Sivathanu, 2020); meta-regression on user data (Blut et al., 2021); surveys on trust/empathy (van Pinxteren et al., 2019).
How PapersFlow Helps You Research Technology Adoption of Service Robots
Discover & Search
Research Agent uses searchPapers and exaSearch to find adoption studies, then citationGraph on Blut et al. (2021) reveals 922-cited meta-analyses linking anthropomorphism to TAM models. findSimilarPapers expands to hospitality contexts like Pillai & Sivathanu (2020).
Analyze & Verify
Analysis Agent applies readPaperContent to Ivanov et al. (2019), then runPythonAnalysis with pandas to meta-analyze survey data on adoption barriers across 419-cited papers. verifyResponse via CoVe and GRADE grading checks trust claims against van Pinxteren et al. (2019), flagging statistical inconsistencies.
Synthesize & Write
Synthesis Agent detects gaps in UTAUT applications for robots via contradiction flagging between Blut et al. (2021) and de Kervenoael et al. (2019). Writing Agent uses latexEditText, latexSyncCitations for TAM models, and latexCompile to generate adoption framework papers with exportMermaid diagrams.
Use Cases
"Run meta-regression on TAM factors from service robot papers using Python."
Research Agent → searchPapers(TAM service robots) → Analysis Agent → runPythonAnalysis(pandas regression on Blut et al. 2021 data) → matplotlib plots of usefulness coefficients.
"Draft LaTeX review on anthropomorphism in hospitality robot adoption."
Synthesis Agent → gap detection(Blut 2021 + Tung 2018) → Writing Agent → latexEditText(structure) → latexSyncCitations(van Pinxteren 2019) → latexCompile(PDF with mermaid adoption flowchart).
"Find GitHub repos with service robot adoption survey code."
Research Agent → searchPapers(Ivanov 2019 code) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect(survey analysis scripts for TAM models).
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers(50+ adoption papers) → citationGraph → GRADE-graded report on UTAUT gaps. DeepScan applies 7-step CoVe to verify Blut et al. (2021) meta-analysis claims with runPythonAnalysis. Theorizer generates hypotheses linking trust to robot empathy from van Pinxteren et al. (2019).
Frequently Asked Questions
What defines technology adoption of service robots?
It applies TAM/UTAUT models to factors like usefulness and trust driving robot use in hospitality and healthcare (Pillai & Sivathanu, 2020).
What are key methods in this subtopic?
Methods include surveys extending TAM, meta-analyses on anthropomorphism, and structural equation modeling of intentions (Blut et al., 2021; de Kervenoael et al., 2019).
What are seminal papers?
Blut et al. (2021, 922 citations) meta-analyzes anthropomorphism; Pillai & Sivathanu (2020, 795 citations) extends TAM for chatbots; van Pinxteren et al. (2019, 473 citations) covers trust.
What open problems exist?
Challenges include longitudinal trust measurement post-deployment and cross-cultural UTAUT validation for physical robots (Ivanov et al., 2019).
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Part of the AI in Service Interactions Research Guide